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Trees and splines in survival analysis

O Intrator1, C Kooperberg

  • 1Department of Statistics, Hebrew University, Jerusalem, Israel.

Statistical Methods in Medical Research
|September 1, 1995
PubMed
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This study compares Survival Trees and HARE, two nonparametric methods for analyzing censored survival data. Both methods offer complementary strengths, providing valuable tools for survival analysis alongside the Cox proportional hazards model.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • The Cox proportional hazards model is a standard for survival data analysis.
  • Nonparametric alternatives are emerging for censored survival data.
  • Regression techniques are being extended to survival analysis.

Purpose of the Study:

  • To compare the strengths and weaknesses of Survival Trees and HARE.
  • To evaluate nonparametric alternatives to the Cox proportional hazards model.
  • To assess the utility of tree-based and spline-based methods in survival analysis.

Main Methods:

  • Discussion of methods based on partition trees (Survival Trees) and polynomial splines (HARE).
  • Analysis of two datasets using both Survival Trees and HARE.

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  • Comparison of the performance and characteristics of the two methods.

    • Main Results:

    • HARE offers implicit proportionality checks and explicit conditional hazards models for graphical summaries.
    • Survival Trees automatically partition data into groups with similar survival histories.
    • Results from Survival Trees and HARE are often complementary, offering different insights.

    Conclusions:

    • Survival Trees and HARE are valuable nonparametric tools for survival data analysis.
    • These methods extend traditional regression techniques to censored data.
    • Both methods provide useful, often complementary, approaches for data analysts.